Clustering of Gaussian probability density functions using centroid neural network

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Clustering of Gaussian probability density functions using centroid neural network

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© IEE
Published

An unsupervised competitive neural network algorithm for clustering mixtures of Gaussian probability density functions is proposed. The algorithm based on centroid neural network with Bhattacharyya distance is evaluated in the context of speech recognition and the results show that it can reduce the Gaussian mixtures by almost 60% over the k-means algorithm.

Inspec keywords: pattern clustering; hidden Markov models; Gaussian distribution; speech recognition; neural nets; unsupervised learning

Other keywords: speech recognition; centroid neural network; Gaussian probability density function; continuous density hidden Markov model; clustering analysis; Bhattacharyya distance; unsupervised competitive learning algorithm

Subjects: Speech recognition and synthesis; Neural nets (theory); Speech recognition; Learning in AI (theory); Markov processes; Markov processes

References

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      • K.W.B. Mak , E. Bocchieri . Subspace distribution clustering hidden Markov model. IEEE Trans. Speech Audio Process. , 3 , 264 - 275
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